无模型的强化学习控制与零分钟屏障功能,用于受限制的系统.
1Robotics and Advanced Manufacturing, Center for Research and Advanced Studies (CINVESTAV), 1062 Industria Metalurgica Av., Ramos Arizpe, 25903, Mexico.
概括
本研究引入了适应性控制器,用于未知的系统,确保在限制范围内安全运行. 这种新的方法平衡了性能和约束满足,没有内部循环.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 控制未知的非亲系离散时间系统,由于固有的动态和物理限制,会带来挑战.
- 在运行约束范围内确保系统稳定性和安全性对于现实应用,如直流电机控制,至关重要.
研究的目的:
- 开发一种适应性输出反控制器,用于未知的非同源离散时间系统.
- 为了最大限度地降低成本,同时强制执行输入,输出和跟踪错误的约束.
- 确保安全运行区域的前向不变性.
主要方法:
- 使用强化学习算法与零分钟屏障函数相结合,以执行约束.
- 通过模糊规则模拟网络实现的受雇主关键网络,用于自适应控制.
- 为了适应式控制器更新而没有内部代,衍生了在线学习规律.
主要成果:
- 对于未知的非同系系统,实现了近乎最佳的跟踪性能.
- 成功执行对称和不对称的约束,确保安全操作.
- 证明了对极端干扰和轨道变化的坚固性.
结论:
- 拟议的自适应控制器有效地平衡了接近最佳的性能与严格的约束满足.
- 该方法通过消除内部代来提高计算效率.
- 在高增益直流电机扭矩控制系统中经过验证的有效性和稳定性.
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